Interpretation of CT scan images: DICOM format

Digital Imaging and Communications in Medicine (#DICOM) is the standard for the communication and management of #medicalimaging information and related data [1].


An image saved in DICOM format contains an image from a medical scan, such as an ultrasound, CT (computed tomography) scan or MR (magnetic resonance) image. DICOM files may also include identification data for patients in its metadata such as Name, Birthday or type of machine that performed the imaging test.


Images stored in this format can be viewed and opened through visualizers that accept this data type, such us 3D slicer [2]. In this visualization, the image appears in a grayscale where each value of gray corresponds to a concrete density level of the organ it belongs. The correspondence between grayscale value and organ density is obtained through the Hounsfield unit (HU).


The Hounsfield Unit (HU) scale is a quantitative scale used for describing radiodensity. It applies a linear transformation where the radiodensity of distilled water at standard pressure and temperature (STP) is defined as 0 HU, while the radiodensity of air at STP is defined as -1000 HU [3].





Figure 1: Example of the visualization of a CT scan image stored in DICOM format [3]



DICOM images typically contain between 12–16 bits/pixel, which corresponds to approximately 4,096 to 65,536 shades of gray [4]. Most medical displays and regular computer screens are often limited to 8 bits or 256 shades of gray.

Most images will require viewing between -1000 HU (which is a reference for air) and +1000 HU (which typically references hard bone).



Figure 2: HU scale, [4]



Analysing medical image tests is not straightforward since, even with a high-resolution screen, the different HU levels present in the image are not visible to the human eye. For this reason, a good processing of these images is required, so that the desired organ can be filtered and treated.



Together with this, visualizers such as the above-mentioned 3D slicer allow the user to vary the contrast of the input image to highlight the different organs present in the test and their borders.





Figure 3: Example of contrast change in a DICOM image using 3D slicer [2]



REFERENCES:


[1] https://en.wikipedia.org/wiki/DICOM


[2] https://www.slicer.org


[3] https://towardsdatascience.com/a-matter-of-grayscale-understanding-dicom-windows-1b44344d92bd


[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3043920/

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